Adjuvant transarterial chemoembolization (TACE) is widely adopted in China for resectable hepatocellular carcinoma (HCC), yet its efficacy remains inconsistent. We aimed to identify factors influencing individual patient benefit using causal machine learning. To this end, we retrospectively collected HCC patients with high risk factors for tumor recurrence from four centers of China, divided into the discovery cohort and the validation cohort . The primary endpoint was disease-free survival (DFS). The primary endpoint was overall survival (OS).Individual treatment effects (ITEs) were estimated within a causal machine learning framework. An ITE \< 0 was considered recommendation for adjuvant TACE , while ITE ≥ 0 indicated active surveillance. The model would be validated in the validation cohort. The contribution of each variable to ITE was assessed using the Shapley Additive Explanations (SHAP). An online calculator would be developed for future use by public.
Study Type
OBSERVATIONAL
Enrollment
1,005
The First Affiliated Hospital of USTC
Hefei, Anhu, China
Disease free survival
Disease-free survival (DFS) was defined as the time from the date of curative-intent HCC surgery to the first occurrence of local recurrence, distant metastasis, or death from any cause, whichever came first. Patients without any of these events were censored at the last follow-up.
Time frame: From January 2018 to December 2023
Overall survival
Overall Survival (OS) was defined as the time from the date of curative-intent HCC surgery to the first occurrence of death from any cause. Patients who were still alive at the last follow-up were censored at the date of the last follow-up.
Time frame: From January 2018 to December 2023
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